Quasars acting as strong gravitational lenses offer a rare opportunity to probe the redshift evolution of scaling relations between supermassive black holes and their host galaxies, particularly the MBH–Mhost relation. Using these powerful probes, the mass of the host galaxy can be precisely inferred from the Einstein radius θE. Using 812,118 quasars from DESI DR1 (0.03 ≤ z ≤ 1.8), we searched for quasars lensing higher-redshift galaxies by identifying background emission-line features in their spectra. To detect these rare systems, we trained a convolutional neural network (CNN) on mock lenses constructed from real DESI spectra of quasars and emission-line galaxies (ELGs), achieving a high classification performance (AUC = 0.99). We also trained a regression network to estimate the redshift of the background ELG. Applying this pipeline, we identified seven high-quality (Grade A) lens candidates, each exhibiting a strong [O II] doublet at a higher redshift than the foreground quasar; four candidates additionally show Hβ, [O III] λ4959, and [O III] λ5007 emission. These results significantly expand the sample of quasar lens candidates beyond the 12 identified and 3 confirmed in previous work and demonstrate the potential for scalable, data-driven discovery of quasars as strong lenses in upcoming spectroscopic surveys.
Quasars Acting as Strong Lenses Found in DESI DR1 / E. Mcarthur, M.M.. - In: THE ASTROPHYSICAL JOURNAL. - ISSN 0004-637X. - 1006:2(2026 Jul 22), pp. 147.1-147.14. [10.3847/1538-4357/ae8014]
Quasars Acting as Strong Lenses Found in DESI DR1
D. Bianchi;
2026
Abstract
Quasars acting as strong gravitational lenses offer a rare opportunity to probe the redshift evolution of scaling relations between supermassive black holes and their host galaxies, particularly the MBH–Mhost relation. Using these powerful probes, the mass of the host galaxy can be precisely inferred from the Einstein radius θE. Using 812,118 quasars from DESI DR1 (0.03 ≤ z ≤ 1.8), we searched for quasars lensing higher-redshift galaxies by identifying background emission-line features in their spectra. To detect these rare systems, we trained a convolutional neural network (CNN) on mock lenses constructed from real DESI spectra of quasars and emission-line galaxies (ELGs), achieving a high classification performance (AUC = 0.99). We also trained a regression network to estimate the redshift of the background ELG. Applying this pipeline, we identified seven high-quality (Grade A) lens candidates, each exhibiting a strong [O II] doublet at a higher redshift than the foreground quasar; four candidates additionally show Hβ, [O III] λ4959, and [O III] λ5007 emission. These results significantly expand the sample of quasar lens candidates beyond the 12 identified and 3 confirmed in previous work and demonstrate the potential for scalable, data-driven discovery of quasars as strong lenses in upcoming spectroscopic surveys.| File | Dimensione | Formato | |
|---|---|---|---|
|
McArthur_2026_ApJ_1006_147.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Licenza:
Creative commons
Dimensione
8.46 MB
Formato
Adobe PDF
|
8.46 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.




